Biology subjects

Kosaki, K.

Publications and source records attributed to Kosaki, K..

3 recordsLinked to original sources

bioRxiv2023

DNA methylation signature in NSD2 loss-of-function variants appeared similar to that in Wolf-Hirschhorn syndrome

PurposeWolf-Hirschhorn syndrome (WHS), a contiguous gene syndrome caused by the hemizygous deletion of the distal short arm of chromosome 4 where NSD2 is, reportedly exhibits specific DNA methylation signatures in peripheral blood cells. However, responsible genomic loci for signatures are unreported. The objective of the study is to define the loci of WHS-related DNA methylation signatures and to explore the role of NSD2 for the signatures. MethodsWe conducted genome-wide methylation analysis of individuals with WHS or NSD2 variants using array. We studied genome-edited knock in mice or induced pluripotent stem cells to explore the function of NSD2 variants which are observed in congenital anomaly cases. ResultsThree undiagnosed cases with NSD2 variants showed WHS-related DNA methylation signatures. These variants were validated to be NSD2 loss-of-function in induced pluripotent stem cells or genome-edited knock-in mice. p.Pro905Leu variant decreased Nsd2 protein levels, and changed Histone H3-Lysine 36 demethylation levels in similar way in the same genomic regions as Nsd2 knock out mice regulated. Nsd2 knock out mice exhibited common DNA methylation changes. ConclusionThese results revealed that WHS-related DNA methylation signatures are dependent on NSD2 dysfunction and are useful in diagnosing NSD2 variants of unknown significance.

genetics↗

bioRxiv2021

Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data

Many disease-associated genomic variants disrupt gene function through abnormal splicing. With the advancement of genomic medicine, identifying disease-associated splicing associated variants has become more important than ever. Most bioinformatics approaches to detect splicing associated variants require both genome and transcriptomic data. However, there are not many datasets where both of them are available. In this study, we developed a methodology to detect genomic variants that cause splicing changes (more specifically, intron retention), using transcriptome sequencing data alone. After demonstrating its high sensitivity and precision, we have applied it to 230,988 transcriptome sequencing data from the publicly available repository and identified 27,937 intron retention associated variants (IRAVs). In addition, by exploring positional relationships with variants registered in existing disease databases, we extracted 3,077 putative disease-associated IRAVs, which range from cancer drivers to variants linked with autosomal recessive disorders. The new in-silico screening framework proposed here provides a foundation for a platform that can automatically acquire medical knowledge making the most of massively accumulated publicly available sequencing data. Collections of IRAVs identified in this study are available through IRAVDB (https://iravdb.io/).

bioinformatics↗

bioRxiv2021

The dynamics of cell-free DNA from urine and blood after a full marathon

PurposeCell-free DNA (cfDNA) has been investigated as a minimally invasive biomarker for many diseases, particularly cancer. An increase in cfDNA has been observed during exercise. Neutrophil extracellular traps (NETs) may be the origin of cfDNA in response to acute exercise, but the mechanisms of generation of cfDNA during exercise remain unclear. In this study we investigated the dynamics of serum and urinary cfDNA levels and determined the relevance of other biomarkers to serum and urinary cfDNA levels and fragment size after a full marathon. MethodsSamples were collected from 23 healthy male subjects. Blood and urine samples were collected before and immediately, two hours, and one day after the full marathon. The measurements included serum and urinary cfDNA, creatine kinase, myoglobin, creatinine, white blood cells, platelets, and lactoferrin from blood, and amylase, albumin, and creatinine from urine. ResultsSerum and urinary cfDNA levels increased after a full marathon. Creatine kinase, myoglobin, and creatinine in blood, and albumin and creatinine in urine also increased significantly after a full marathon. Serum cfDNA showed peak values about 180 bp after the full marathon. Values over 1000 bp were present at two hours post-marathon. Urinary cfDNA showed peak values from 35 bp to 50 bp after the full marathon. Values over 1000 bp appeared at Immediately and two hours post marathon. ConclusionThis study revealed that both serum and urinary cfDNA levels transiently increased after a full marathon. In addition, these cfDNA fragment varied in size.

physiology↗